Utilizing Sarima For Seasonal Forecasting Of Coffee Production In Aceh Province, Indonesia
Keywords:
Coffee, Data Mining, Forecasting, SARIMA, MAPEAbstract
In this paper, we forecast coffee production in Aceh Province, Indonesia, using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. Coffee is a critical commodity for the region’s economy, contributing significantly to both local income and export revenues. Accurate forecasting of coffee production is essential for economic planning, supply chain management, and strategic development in the coffee sector. Using secondary data from the Indonesian Central Bureau of Statistics, we identified the SARIMA (2,0,1)(1,1,1)12 model as the best fit, with a Mean Absolute Percentage Error (MAPE) of 12.94%. The forecasting results for the period of 2023 to 2024 reveal a consistent seasonal pattern in line with historical data, though a slight decline in production is projected. Notably, the lowest production of 22 tons occurred in February 2019, while the highest, 21,408 tons, was recorded in July 2019. These findings provide valuable insights for policymakers and stakeholders in the coffee industry, offering a robust basis for developing targeted interventions to enhance production and manage fluctuations. The results underscore the importance of reliable forecasting models like SARIMA in supporting sustainable growth and decision-making in regional agricultural sectors.
References
[1] E. D. Martauli, “Analysis Of Coffee Production In Indonesia,” JASc (Journal Agribus. Sci., vol. 1, no. 2, pp. 112–120, 2018, doi: 10.30596/jasc.v1i2.1962.
[2] BPS (Badan Pusat Statistik), “Statistik Kopi Indonesia 2022,” vol. 7, 2023, [Online]. Available: https://www.bps.go.id/id/publication/2023/11/30/abde293e6c0fc5d45aaa9fe8/statistik-kopi-indonesia-2022.html
[3] S. Ma, Q. Liu, and Y. Zhang, “A prediction method of fire frequency: Based on the optimization of SARIMA model,” PLoS One, vol. 16, no. 8 August, pp. 1–13, 2021, doi: 10.1371/journal.pone.0255857.
[4] M. Ibnu, A. Offermans, and P. Glasbergen, “Toward a more sustainable coffee production,” Pelita Perkeb. (a Coffee Cocoa Res. Journal), vol. 35, no. 3, pp. 212–229, 2020, doi: 10.22302/iccri.jur.pelitaperkebunan.v35i3.361.
[5] M. I. Sulaiman et al., “Making biodiversity work for coffee production. A case study of Gayo Arabica coffee in Indonesia,” MOJ Ecol. Environ. Sci., vol. 6, no. 4, pp. 156–162, 2021, doi: 10.15406/mojes.2021.06.00228.
[6] A. Anhar, U. H. A. Rasyid, A. M. Muslih, A. Baihaqi, Romano, and Y. Abubakar, “Sustainable Arabica coffee development strategies in Aceh, Indonesia,” IOP Conf. Ser. Earth Environ. Sci., vol. 667, no. 1, 2021, doi: 10.1088/1755-1315/667/1/012106.
[7] V. Byrareddy, L. Kouadio, S. Mushtaq, and R. Stone, “Sustainable production of robusta coffee under a changing climate: A 10-year monitoring of fertilizer management in coffee farms in Vietnam and Indonesia,” Agronomy, vol. 9, no. 9, 2019, doi: 10.3390/agronomy9090499.
[8] B. Lim and S. Zohren, “Time-series forecasting with deep learning: A survey,” Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., vol. 379, no. 2194, 2021, doi: 10.1098/rsta.2020.0209.
[9] N. Deretić, D. Stanimirović, M. Al Awadh, N. Vujanović, and A. Djukić, “SARIMA Modelling Approach for Forecasting of Traffic Accidents,” Sustain., vol. 14, no. 8, 2022, doi: 10.3390/su14084403.
[10] M. Shad, Y. D. Sharma, and A. Singh, “Forecasting of monthly relative humidity in Delhi, India, using SARIMA and ANN models,” Model. Earth Syst. Environ., vol. 8, no. 4, pp. 4843–4851, 2022, doi: 10.1007/s40808-022-01385-8.
[11] G. R. Alonso Brito, A. Rivero Villaverde, A. Lau Quan, and M. E. Ruíz Pérez, “Comparison between SARIMA and Holt–Winters models for forecasting monthly streamflow in the western region of Cuba,” SN Appl. Sci., vol. 3, no. 6, 2021, doi: 10.1007/s42452-021-04667-5.
[12] D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput. Sci., vol. 7, pp. 1–24, 2021, doi: 10.7717/PEERJ-CS.623.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Aprian Gigin Prasetia, Muhammad Fikry, Yesy Afrillia
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright Notice
Authors published in this journal agree to the following terms:
1. The copyright of each article is retained by the author (s).
2. The author grants the journal the first publication rights with the work simultaneously licensed under the Creative Commons Attribution License, allowing others to share the work with an acknowledgment of authorship and the initial publication in this journal.
3. Authors may enter into separate additional contractual agreements for the non-exclusive distribution of published journal versions of the work (for example, posting them to institutional repositories or publishing them in a book), with acknowledgment of their initial publication in this journal.
4. Authors are permitted and encouraged to post their work online (For example in the Institutional Repository or on their website) before and during the submission process, as this can lead to productive exchanges, as well as earlier and larger citations of published work.
5. Articles and all related material published are distributed under a Creative Commons Attribution-ShareAlike 4.0 International License.
